{"product_id":"machine-learning-with-pytorch-and-scikit-learn-develop-machine-learning-and-deep-learning-models-with-python-hardcover","title":"Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python - Hardcover","description":"\u003cp\u003eby \u003cb\u003eSebastian Raschka\u003c\/b\u003e (Author), \u003cb\u003eYuxi (Hayden) Liu\u003c\/b\u003e (Author), \u003cb\u003eVahid Mirjalili\u003c\/b\u003e (Foreword by)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eThis book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePurchase of the print or Kindle book includes a free eBook in PDF format.\u003c\/strong\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eLearn applied machine learning with a solid foundation in theory\u003c\/li\u003e\n\u003cli\u003eClear, intuitive explanations take you deep into the theory and practice of Python machine learning\u003c\/li\u003e\n\u003cli\u003eFully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003eMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems.\u003c\/p\u003e\u003cp\u003ePacked with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.\u003c\/p\u003e\u003cp\u003eWhy PyTorch?\u003c\/p\u003e\u003cp\u003ePyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.\u003c\/p\u003e\u003cp\u003eYou will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).\u003c\/p\u003e\u003cp\u003eThis PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eExplore frameworks, models, and techniques for machines to 'learn' from data\u003c\/li\u003e\n\u003cli\u003eUse scikit-learn for machine learning and PyTorch for deep learning\u003c\/li\u003e\n\u003cli\u003eTrain machine learning classifiers on images, text, and more\u003c\/li\u003e\n\u003cli\u003eBuild and train neural networks, transformers, and boosting algorithms\u003c\/li\u003e\n\u003cli\u003eDiscover best practices for evaluating and tuning models\u003c\/li\u003e\n\u003cli\u003ePredict continuous target outcomes using regression analysis\u003c\/li\u003e\n\u003cli\u003eDig deeper into textual and social media data using sentiment analysis\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eIf you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.\u003c\/p\u003e\u003cp\u003eBefore you get started with this book, you'll need a good understanding of calculus, as well as linear algebra.\u003c\/p\u003eTable of Contents\u003col\u003e\n\u003cli\u003eGiving Computers the Ability to Learn from Data\u003c\/li\u003e\n\u003cli\u003eTraining Simple Machine Learning Algorithms for Classification\u003c\/li\u003e\n\u003cli\u003eA Tour of Machine Learning Classifiers Using Scikit-Learn\u003c\/li\u003e\n\u003cli\u003eBuilding Good Training Datasets - Data Preprocessing\u003c\/li\u003e\n\u003cli\u003eCompressing Data via Dimensionality Reduction\u003c\/li\u003e\n\u003cli\u003eLearning Best Practices for Model Evaluation and Hyperparameter Tuning\u003c\/li\u003e\n\u003cli\u003eCombining Different Models for Ensemble Learning\u003c\/li\u003e\n\u003cli\u003eApplying Machine Learning to Sentiment Analysis\u003c\/li\u003e\n\u003cli\u003ePredicting Continuous Target Variables with Regression Analysis\u003c\/li\u003e\n\u003cli\u003eWorking with Unlabeled Data - Clustering Analysis\u003c\/li\u003e\n\u003cli\u003eImplementing a Multilayer Artificial Neural Network from Scratch\u003c\/li\u003e\n\u003c\/ol\u003e\u003cp\u003e(N.B. Please use the Look Inside option to see further chapters)\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 774\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.63 x 9.25 x 7.5 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e February 25, 2022\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42290149228679,"sku":"9781837021956","price":115.18,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0601\/2623\/2711\/files\/82e8937d69b2f1b774176c2610c9b16a.webp?v=1737965587","url":"https:\/\/booksby.splitshops.com\/products\/machine-learning-with-pytorch-and-scikit-learn-develop-machine-learning-and-deep-learning-models-with-python-hardcover","provider":"Books by splitShops","version":"1.0","type":"link"}